Depending on what your business needs, you can choose to leave the data as is (E.g. In addition, they can use the same sales data and social media trends in the data lake to build intelligent machine learning models for personalized recommendations on their website. Plan the directory structure to account for elements like organizational unit, data source, timeframe, and processing requirements. Under construction, looking for contributions, In this section, we will address how to optimize your data lake store for your performance in your analytics pipeline. What portion of your data do you run your analytics workloads on? /raw/sensordata /raw/lobappdata /raw/userclickdata, /workspace/salesBI /workspace/manufacturindatascience. data science notebooks) or through a data warehouse. azure 2wtech justpaste As you are building your enterprise data lake on ADLS Gen2, its important to understand your requirements around your key use cases, including. This allows you to query your logs using KQL and author queries which enumerate the. This section provides key considerations that you can use to manage and optimize the cost of your data lake. Cloud Volumes ONTAP supports up to a capacity of 368TB, and supports various use cases such as file services, databases, DevOps or any other enterprise workload, with a strong set of features including high availability, data protection, storage efficiencies, Kubernetes integration, and more. For more details, see: Optimize for high throughput target getting at least a few MBs (higher the better) per transaction. E.g. In a lot of cases, if your raw data (from various sources) itself is not large, you have the following options to ensure the data set your analytics engines operate on is still optimized with large file sizes. Another optional component is Azure HDInsight, which lets you run distributed big data jobs using tools like Hadoop and Spark. What are the various transaction patterns on the analytics workloads? databricks data azure streamsets partners aws isv workload migration scalability unified analytics Azure Storage logs in Azure Monitor can be enabled through the Azure Portal, PowerShell, the Azure CLI, and Azure Resource Manager templates. There are 2 types of ACLs Access ADLs that control access to a file or a directory, Default ACLs are templates of ACLs set for directories that are associated with a directory, a snapshot of these ACLs are inherited by any child items that are created under that directory. The solution integrates Blob Storage with Azure Data Factory, a tool for creating and running extract, transform, load (ETL) and extract, load and transform (ELT) processes. When deciding the structure of your data, consider both the semantics of the data itself as well as the consumers who access the data to identify the right data organization strategy for you. E.g. A folder also has access control lists (ACLs) associated with it, there are two types of ACLs associated with a folder access ACLs and default ACLs, you can read more about them here. Create different folders or containers (more below on considerations between folders vs containers) for the different data zones - raw, enriched, curated and workspace data sets. The table below provides a framework for you to think about the different zones of the data and the associated management of the zones with a commonly observed pattern. Let us take our Contoso.com example where they have analytics scenarios to manage the company operations. Cross resource RBACs at subscription or resource group level. This data is stored as is in the data lake and is consumed by an analytics engine such as Spark to perform cleansing and enrichment operations to generate the curated data. Avro format is favored by message bus such as Event Hub or Kafka writes multiple events/messages in succession. Apache Parquet is an open source file format that is optimized for read heavy analytics pipelines. Inside a zone, choose to organize data in folders according to logical separation, e.g. This creates a management problem of what is the source of truth and how fresh it needs to be, and also consumes transactions involved in copying data back and forth. Curated data: This layer of data contains the high value information that is served to the consumers of the data the BI analysts and the data scientists. Once enriched data is generated, can be moved to a cooler tier of storage to manage costs. In addition to managing access using AAD identities using RBACs and ACLs, ADLS Gen2 also supports using SAS tokens and shared keys for managing access to data in your Gen2 account. Data assets in this layer is usually highly governed and well documented. With little or no centralized control, so will the associated costs increase. There are scenarios where enterprise data lakes serve multiple customer (internal/external) scenarios that may be subject to different requirements different query patterns and different access requirements. There are multiple approaches to organizing the data in a data lake, this section documents a common approach that has been adopted by many customers building a data platform. Folder structure mirrors organization, e.g. Workspace data: In addition to the data that is ingested by the data engineering team from the source, the consumers of the data can also choose to bring other data sets that could be valuable. RBACs let you assign roles to security principals (user, group, service principal or managed identity in AAD) and these roles are associated with sets of permissions to the data in your container. If you are not able to pick an option that perfectly fits your scenarios, we recommend that you do a proof of concept (PoC) with a few options to let the data guide your decision. For specific security principals you want to provide permissions, add them to the security group instead of creating specific ACLs for them. azure storage data options blob All of these are machine-readable binary file formats, offer compression to manage the file size and are self-describing in nature with a schema embedded in the file. In these cases, having a metastore is helpful for discovery. E.g. ADLS Gen2 offers faster performance and Hadoop compatible access with the hierarchical namespace, lower cost and security with fine grained access controls and native AAD integration. Driven by global markets and/or geographically distributed organizations, there are scenarios where enterprises have their analytics scenarios factoring multiple geographic regions. Hadoop has a set of file formats it supports for optimized storage and processing of structured data. The data itself can be categorized into two broad categories. all the data in the past 12 hours), the partitioning scheme (in this case, done by datetime) lets you skip over the irrelevant data and only seek the data that you want. This document captures these considerations and best practices that we have learnt based on working with our customers. The pricing for ADLS Gen2 can be found here. Azure Storage logs in Azure Monitor is a new preview feature for Azure Storage which allows for a direct integration between your storage accounts and Log Analytics, Event Hubs, and archival of logs to another storage account utilizing standard diagnostic settings. In addition, Cloud Volumes ONTAP provides storage efficiency features, including thin provisioning, data compression, and deduplication, reducing the storage footprint and costs by up to 70%. synapse analytics machine dataverse microsoft obungi integration apache A single storage account gives you the ability to manage a single set of control plane management operations such as RBACs, firewall settings, data lifecycle management policies for all the data in your storage account, while allowing you to organize your data using containers, files and folders on the storage account. While at a higher level, they both are used for logical organizations of the data, they have a few key differences. We do request that when you have a scenario where you have requirements for really storing really large amounts of data (multi-petabytes) and require the account to support a really large transaction and throughput pattern (tens of thousands of TPS and hundreds of Gbps throughput), typically observed by requiring 1000s of cores of compute power for analytics processing via Databricks or HDInsight, please do contact our product group so we can plan to support your requirements appropriately. While ADLS Gen2 supports storing all kinds of data without imposing any restrictions, it is better to think about data formats to maximize efficiency of your processing pipelines and optimize costs you can achieve both of these by picking the right format and the right file sizes. A very common point of discussion as we work with our customers to build their data lake strategy is how they can best organize their data. Serverless analytics based on Azure Spark, Managed serverless NoSQL data store, supporting Cassandra and MongoDB, Storing key-value pairs with no fixed schema, Storing relational datasets with SQL querying, Cloud-based enterprise data warehouse (EDW), Storing large volumes of structured data, enabling massively parallel processing (MPP), Analytics engine based on SQL Server Analysis Server, Building ad-hoc semantic models for tabular data, Integrating the data lake with over 50 storage systems and databases, transforming data, Related content: read our guide to Azure Analytics Services. Resource: A manageable item that is available through Azure. E.g. Azure Data Lake is a big data solution based on multiple cloud services in the Microsoft Azure ecosystem. This is part of our series of articles onAzure big data. Related content: read our guide to Azure High Availability. You will create the /logs directory and create two AAD groups LogsWriter and LogsReader with the following permissions. A reference of the the full list of metrics and resources logs and their associated schema can be found in the Azure Storage monitoring data reference. Azure Data Lake Storage Gen2 provides Portable Operating System Interface (POSIX) access control for users, groups, and service principals defined in Azure Active Directory (Azure AD). How much data am I storing in the data lake? You can read more about storage accounts here. In simplistic terms, partitioning is a way of organizing your data by grouping datasets with similar attributes together in a storage entity, such as a folder. Its worth noting that while all this data layers are present in a single logical data lake, they could be spread across different physical storage accounts. Use access control to create default permissions that can be automatically applied to new files or directories. A common question our customers ask us is if they can build their data lake in a single storage account or if they need multiple storage accounts. There are properties that can be applied at a container level such as RBACs and SAS keys. Please remember that this single data store is a logical entity that could manifest either as a single ADLS Gen2 account or as multiple accounts depending on the design considerations. What are the various analytics workloads that Im going to run on my data lake? E.g. Consider the analytics consumption patterns when designing your folder structures. Parquet is one such prevalent data format that is worth exploring for your big data analytics pipeline. When is ADLS Gen2 the right choice for your data lake? In addition to improving performance by filtering the specific data used by the query, Query Acceleration also lowers the overall cost of your analytics pipeline by optimizing the data transferred, and hence reducing the overall storage transaction costs, and also saving you the cost of compute resources you would have otherwise spun up to read the entire dataset and filter for the subset of data that you need. Key considerations in designing your data lake, Organizing and managing data in your data lake. We will improve this document to include more analytics patterns in future iterations. As an example , think of the raw data as a lake/pond with water in its natural state, the data is ingested and stored as is without transformations, the enriched data is water in a reservoir that is cleaned and stored in a predictable state (schematized in the case of our data), the curated data is like bottled water that is ready for consumption. You can use the Cool and Archive tiers in ADLS Gen2 to store this data. If you are considering a federated data lake strategy with each organization or business unit having their own set of manageability requirements, then this model might work best for you. E.g. In general, its a best practice to organize your data into larger sized files (target at least 100 MB or more) for better performance. edi protocol parser If you want to access your logs through another query engine such as. RBACs can help manage roles related to control plane operations (such as adding other users and assigning roles, manage encryption settings, firewall rules etc) or for data plane operations (such as creating containers, reading and writing data etc). A storage account has no limits on the number of containers, and the container can store an unlimited number of folders and files. There is still one centralized logical data lake with a central set of infrastructure management, data governance and other operations that comprises of multiple storage accounts here. The data in the raw zone is sometimes also stored as an aggregated data set, e.g. There are no limits on how many folders or files can be created under a folder. Contoso is trying to project their sales targets for the next fiscal year and want to get the sales data from their various regions. Workspace data is like a laboratory where scientists can bring their own for testing. Fore more information on RBACs, you can read this article. log messages from servers) or aggregate it (E.g. It lets you leverage these open source projects, with fully managed infrastructure and cluster management, and no need for installation and customization. a Data Science team is trying to determine the product placement strategy for a new region, they could bring other data sets such as customer demographics and data on usage of other similar products from that region and use the high value sales insights data to analyze the product market fit and the offering strategy. this would be enriched sales data ensuring that the sales data is schematized, enriched with other product or inventory information and also separated into multiple datasets for the different business units inside Contoso. The overall performance of your analytics pipeline would have considerations specific to the analytics engines in addition to the storage performance consideration, our partnerships with the analytics offerings on Azure such as Azure Synapse Analytics, HDInsight and Azure Databricks ensure that we focus on making the overall experience better. Virtual machines, storage accounts, VNETs are examples of resources. Factors to consider when picking the option that works for you. We would like to anchor the rest of this document in the following structure for a few key design/architecture questions that we have heard consistently from our customers. Some customers have end to end ownership of the components of an analytics pipeline, and other customers have a central team/ organization managing the infrastructure, operations and governance of the data lake while serving multiple customers either other organizations in their enterprise or other customers external to their enterprise. analytics ci azure cd lake data assume configuration nuget restore task packages package build below into stack In this case, they could choose to create different data lakes for the various data sources. A data lake solution in Azure typically consists of four building blocks. Another common questions that our customers ask if when to use containers and when to use folders to organize the data. When using RBAC at the container level as the only mechanism for data access control, be cautious of the 2000 limit, particularly if you are likely to have a large number of containers. ACLs let you manage a specific set of permissions for a security principal to a much narrower scope a file or a directory in ADLS Gen2. Further, when you have files that are too small (in the KBs range), the amount of throughput you achieve with the I/O operations is also low, requiring more I/Os to get the data you want. RBACs are essentially scoped to top-level resources either storage accounts or containers in ADLS Gen2. In addition, since the similar data types (for a column) are stored together, Parquet lends itself friendly to efficient data compression and encoding schemes lowering your data storage costs as well, compared to storing the same data in a text file format. Putting the date at the end means that you can restrict specific date ranges without having to process many subdirectories unnecessarily. Data that can be shared globally across all regions E.g. This ability to skip also results in only the data you want being sent from the storage to the analytics engine resulting in lower cost along with better performance. Folder/Directory: A folder (also referred to as a directory) organizes a set of objects (other folders or files). 32 ACLs (effectively 28 ACLs) per file, 32 ACLs (effectively 28 ACLs) per folder, default and access ACLs each. The SPNs/MSIs for ADF as well as the users and the service engineering team can be added to the LogsWriter group. LogsWriter added to the ACLs of the /logs folder with rwx permissions. Consider the workload's target recovery time objective (RTO) and recovery point objective (RPO). You can find more information about the access control here. As we continue to work with our customers to unlock key insights out of their data using ADLS Gen2, we have identified a few key patterns and considerations that help them effectively utilize ADLS Gen2 in large scale Big Data platform architectures. Storage accounts, containers. Open source computing frameworks such as Apache Spark provide native support for partitioning schemes that you can leverage in your big data application. Now, you have various options of storing the data, including (but not limited to) the ones listed below : If a high priority scenario is to understand the health of the sensors based on the values they send to ensure the sensors are working fine, then you would have analytics pipelines running every hour or so to triangulate data from a specific sensor with data from other sensors to ensure they are working fine. The SPNs/MSIs for Databricks will be added to the LogsReader group. For illustration, we will take the example of a large retail customer, Contoso.com, building out their data lake strategy to help with various predictive analytics scenarios. Contoso wants to provide a personalized buyer experience based on their profile and buying patterns. Subscription: An Azure subscription is a logical entity that is used to separate the administration and financial (billing) logic of your Azure resources. You can read more about these policies, Ensure that you are choosing the right replication option for your accounts, you can read the, Being able to audit your data lake in terms of frequent operations, Having visibiliy into key performace indicators such as operations with high latency, Undestanding common errors, the operations that caused the error, and operations which cause service-side throttling. Storage account: An Azure resource that contains all of your Azure Storage data objects: blobs, files, queues, tables and disks. At the folder level, you can set fine grained access controls using ACLs. When we say hyperscale, we are typically referring to multi-petabytes of data and hundreds of Gbps in throughput the challenges involved with this kind of analytics is very different from a few hundred GB of data and a few Gbps of transactions in throughput. You can view the number of role assigments per subscription in any of the access control (IAM) blades in the portal. Raw data: This is data as it comes from the source systems. datetime or business units or both. You can also use this opportunity to store data in a read-optimized format such as Parquet for downstream processing. As a pre-requisite to optimizations, it is important for you to understand more about the transaction profile and data organization. Azure Data Lake Storage Gen2 (ADLS Gen2) is a highly scalable and cost-effective data lake solution for big data analytics. One common question that our customers ask is if a single storage account can infinitely continue to scale to their data, transaction and throughput needs. At the container level, you can set coarse grained access controls using RBACs. Azure Data Lake Storage has a capability called Query Acceleration available in preview that is intended to optimize your performance while lowering the cost. ADLS Gen2 supports access control models that combine both RBACs and ACLs to manage access to the data. You can read more about our data lifecycle management policies to identify a plan that works for you. As we have already talked about, optimizing your storage I/O patterns can largely benefit the overall performance of your analytics pipeline. This data has structure and can be served to the consumers either as is (E.g. A subscription is associated with limits and quotas on Azure resources, you can read about them here. Following this practice will help you minimize the process of managing access for new identities which would take a really long time if you want to add the new identity to every single file and folder in your container recursively. Consider the access control model you would want to follow when deciding your folder structures. Start your design approach with one storage account and think about reasons why you need multiple storage accounts (isolation, region based requirements etc) instead of the other way around. In this case, they have various data sources employee data, customers/campaign data and financial data that are subject to different governance and access rules and are also possibly managed by different organizations within the company. Given the varied nature of analytics scenarios, the optimizations depend on your analytics pipeline, storage I/O patterns and the data sets you operate on, specifically the following aspects of your data lake. ADLS Gen2 provides policy management that you can use to leverage the lifecycle of data stored in your Gen2 account. At a container level, you can enable anonymous access (via shared keys) or set SAS keys specific to the container. In this scenario, the customer would provision region-specific storage accounts to store data for a particular region and allow sharing of specific data with other regions. As our enterprise customers serve the needs of multiple organizations including analytics use-cases on a central data lake, their data and transactions tend to increase dramatically. Create security groups for the level of permissions you want for an object (typically a directory from what we have seen with our customers) and add them to the ACLs. A common question that we hear from our customers is when to use RBACs and when to use ACLs to manage access to the data. Where your choose to store your logs from Azure Storage logs becomes important when you consider how you will access it: If you want to access your logs in near real-time and be able to correlate events in logs with other metrics from Azure Monitor, you can store your logs in a Log Analytics workspace. Archive data: This is your organizations data vault - that has data stored to primarily comply with retention policies and has very restrictive usage, such as supporting audits. E.g. Two common patterns where we see this kind of data growth is :-. Beyond this, organizations can optionally use Azure Data Lake Storage, a specialized storage service for large-scale datasets, and Azure Data Lake Analytics, a compute service that processes large scale data sets using T-SQL. azure excel natively datawarehouse Data that needs to be isolated to a region E.g. This document assumes that you have an account in Azure. In this case, the data platform can allocate a workspace for these consumers so they can use the curated data along with the other data sets they bring to generate valuable insights. The columnar storage structure of Parquet lets you skip over non-relevant data making your queries much more efficient. this would be raw sales data that is ingested from Contosos sales management tool that is running in their on-prem systems. Let us take an example where you have a directory, /logs, in your data lake with log data from your server. A folder does not support non-AAD access control. Object/file: A file is an entity that holds data that can be read/written. This lets you use POSIX permissions to lock down specific regions or data time frames to certain users. The ACLs apply to the folder only (unless you use default ACLs, in which case, they are snapshotted when new files/folders are created under the folder). This lends itself as the choice for your enterprise data lake focused on big data analytics scenarios extracting high value structured data out of unstructured data using transformations, advanced analytics using machine learning or real time data ingestion and analytics for fast insights. Parquet and ORC file formats are favored when the I/O patterns are more read heavy and/or when the query patterns are focused on a subset of columns in the records where the read transactions can be optimized to retrieve specific columns instead of reading the entire record. Depending on the retention policies of your enterprise, this data is either stored as is for the period required by the retention policy or it can be deleted when you think the data is of no more use. The goal of the enterprise data lake is to eliminate data silos (where the data can only be accessed by one part of your organization) and promote a single storage layer that can accommodate the various data needs of the organization For more information on picking the right storage for your solution, please visit the Choosing a big data storage technology in Azure article. high-quality sales data (that is data in the enriched data zone correlated with other demand forecasting signals such as social media trending patterns) for a business unit that is used for predictive analytics on determining the sales projections for the next fiscal year. Given this is customer data, there are sovereignty requirements that need to be met, so the data cannot leave the region. It provides a platform for .NET developers to effectively process up to petabytes of data. A data warehouse is a store for highly structured schematized data that is usually organized and processed to derive very specific insights. Create different storage accounts (ideally in different subscriptions) for your development and production environments. In this section, we have addressed our thoughts and recommendations on the common set of questions that we hear from our customers as they design their enterprise data lake. Optimizing your data lake for better scale and performance, Choosing a big data storage technology in Azure, Data engineering team, with adhoc access patterns by the Data scientists/BI analysts, Data engineers, BI analysts, Data scientists, Locked for access by data engineering team, Full control to data engineering team, with read access to the BI analysts/data scientists, Full control to data engineering team, with read and write access to the BI analysts/data scientists, Full control to data engineers, data scientists/Bi analysts. Azure Data Lake Analytics allows users to run analytics jobs of any size, leveraging U-SQL to perform analytics tasks that combine C# and SQL. Its worth noting that we have seen customers have different definition of what hyperscale means this depends on the data stored, the number of transactions and the throughput of the transactions.